An Intelligent Diagnosis Flu System Based on Adaptive Neuro-Fuzzy Classifer

This study adopts existing three adaptive-neuro-fuzzy classifiers which are neuro-fuzzy classifier with a scaled conjugate gradient algorithm (NFCSCG), neuro-fuzzy classifier with linguistic hedges (NFCLH) and linguistic hedges neuro-fuzzy classifier with selected features (LHNFCSF) to develop an in...

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Published in:International Symposium on Computing and Networking (Online) pp. 547 - 550
Main Authors: Hsieh, Sheng-Ta, Lin, Chun-Ling
Format: Conference Proceeding Journal Article
Language:English
Published: IEEE 01.12.2015
Subjects:
Accuracy
Adaptive neuro-fuzzy classifer
Adaptive systems
Algorithms
Artificial intelligence
Artificial neural networks
Classifiers
Conjugate gradients
Diagnosis
Fuzzy logic
Influenza
Leave-one-subject-o
Linguistic hedges neuro-fuzzy classifier with selected features (LHNFCSF)
Linguistics
Neuro-fuzzy classifier with linguistic hedges (NFCLH)
Neurofuzzy classifier with a scaled conjugate gradient algorithm (NFCSCG)
Pragmatics
Testing
Training
ISSN:2379-1896
Online Access:Get full text
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Abstract This study adopts existing three adaptive-neuro-fuzzy classifiers which are neuro-fuzzy classifier with a scaled conjugate gradient algorithm (NFCSCG), neuro-fuzzy classifier with linguistic hedges (NFCLH) and linguistic hedges neuro-fuzzy classifier with selected features (LHNFCSF) to develop an intelligent diagnosis flu system. Gaussian membership function is used for fuzzy set descriptions. Leave-one-subject-out (LOSO) cross-validation is used to estimate the performance of three neuro-fuzzy classifiers. The results shows NFCSCG, NFCLF and LHNFCSF achieved the high accuracy of 100% in the training data. In the testing data, the overall accuracies of LHNFCSF achieved 100%, which is superior to other methods. Thus, this study suggests that LHNFCSF in the intelligent diagnosis flu system can provide a preliminary result to physicians so that the doctor could quickly and accurately decide whether patient have cold or flu.
AbstractList This study adopts existing three adaptive-neuro-fuzzy classifiers which are neuro-fuzzy classifier with a scaled conjugate gradient algorithm (NFCSCG), neuro-fuzzy classifier with linguistic hedges (NFCLH) and linguistic hedges neuro-fuzzy classifier with selected features (LHNFCSF) to develop an intelligent diagnosis flu system. Gaussian membership function is used for fuzzy set descriptions. Leave-one-subject-out (LOSO) cross-validation is used to estimate the performance of three neuro-fuzzy classifiers. The results shows NFCSCG, NFCLF and LHNFCSF achieved the high accuracy of 100% in the training data. In the testing data, the overall accuracies of LHNFCSF achieved 100%, which is superior to other methods. Thus, this study suggests that LHNFCSF in the intelligent diagnosis flu system can provide a preliminary result to physicians so that the doctor could quickly and accurately decide whether patient have cold or flu.
Author Sheng-Ta Hsieh
Chun-Ling Lin
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SubjectTerms Accuracy
Adaptive neuro-fuzzy classifer
Adaptive systems
Algorithms
Artificial intelligence
Artificial neural networks
Classifiers
Conjugate gradients
Diagnosis
Fuzzy logic
Influenza
Leave-one-subject-o
Linguistic hedges neuro-fuzzy classifier with selected features (LHNFCSF)
Linguistics
Neuro-fuzzy classifier with linguistic hedges (NFCLH)
Neurofuzzy classifier with a scaled conjugate gradient algorithm (NFCSCG)
Pragmatics
Testing
Training
Title An Intelligent Diagnosis Flu System Based on Adaptive Neuro-Fuzzy Classifer
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